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Vertical Federated Learning: Challenges, Methodologies and Experiments
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Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user devices, however with the increasing concerns on users' privacy. As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients. These sub-models are trained locally by vertically partitioned data with distinct attributes. Therefore, the design of VFL is fundamentally different from that of conventional FL, raising new and unique research issues. In this paper, we aim to discuss key challenges in VFL with effective solutions, and conduct experiments on real-life datasets to shed light on these issues. Specifically, we first propose a general framework on VFL, and highlight the key differences between VFL and conventional FL. Then, we discuss research challenges rooted in VFL systems under four aspects, i.e., security and privacy risks, expensive computation and communication costs, possible structural damage caused by model splitting, and system heterogeneity. Afterwards, we develop solutions to addressing the aforementioned challenges, and conduct extensive experiments to showcase the effectiveness of our proposed solutions.
Forward citations
Cited by 5 Pith papers
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Unlearning Clients, Features and Samples in Vertical Federated Learning
VFU-KD and VFU-GA unlearn clients, features, and samples in vertical federated learning with no communication during unlearning, using stored embeddings.
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Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
Proto-EVFL selects useful unaligned data in vertical federated learning with a dual optimal transport cost and class priors, then aggregates party features with learned gates, improving accuracy on rare and unseen classes.
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Event-Driven Online Vertical Federated Learning
The authors introduce event-driven online vertical federated learning with dynamic local regret, claiming an O(T^{3/4}) regret bound and empirically demonstrating stability under non-stationary streams.
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Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things
DAO-VFL integrates online vertical federated learning with server-side denoising and reinforcement-learning-selected local iteration counts, reporting a regret bound plus experiments on CIFAR-10 and C-MAPSS.
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Attributed Graph Clustering in Collaborative Settings
A collaborative method clusters attributed graphs under vertical data partitioning, using local k-means and secure aggregation to match centralized accuracy with lower communication cost.
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